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Research on shadow elimination in intelligent traffic monitoring

机译：智能交通监控中的阴影消除技术研究

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In road monitoring and traffic analysis systems, we usually utilize video technique to extract, manage, and track targets in the entire scene. However, due to the emergence of shadow, especially the moving shadow, which can result in error in correctly positioning, measuring, and detecting the moving targets. This article proposes a shadow elimination method based on the combination of statistics information and texture feature. According to a lot of traffic images, the range of shadow gray value of traffic road and the range of difference between shadow and background are counted as a threshold. Combining the threshold with LBP histogram, the shadow is segmented and eliminated. In the shadow elimination process, we utilize parallel computing, which can divide the image into parts and deal with them at the same time, shortening the processing time. Compared with the traditional methods, the proposed method improves the processing speed and accuracy greatly. It's helpful to the real-time detection and tracking of moving objects in intelligent traffic monitoring systems.
机译：在道路监控和交通分析系统中，我们通常利用视频技术来提取，管理和跟踪整个场景中的目标。但是，由于阴影的出现，特别是运动阴影的出现，可能导致正确定位，测量和检测运动目标时出现错误。本文提出了一种基于统计信息和纹理特征相结合的阴影消除方法。根据大量的交通图像，将交通道路的阴影灰度值范围和阴影与背景之间的差异范围作为阈值。将阈值与LBP直方图相结合，可以对阴影进行分割和消除。在阴影消除过程中，我们利用并行计算，可以将图像分为多个部分并同时进行处理，从而缩短了处理时间。与传统方法相比，该方法大大提高了处理速度和精度。它有助于智能交通监控系统中运动物体的实时检测和跟踪。

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